predict.cv.stepSplitReg {stepSplitReg}

Predictions for cv.stepSplitReg Object

Description

predict.cv.stepSplitReg returns the predictions for a cv.stepSplitReg object.

Usage

## S3 method for class 'cv.stepSplitReg'
predict(object, newx, group_index = group_index, ...)

Arguments

object

An object of class cv.stepSplitReg

newx

New data for predictions.

group_index

Groups included in the ensemble. Default setting includes all the groups.

...

Additional arguments for compatibility.

Value

The predictions for the cv.stepSplitReg object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

cv.stepSplitReg

Examples

# Required Libraries
library(mvnfast)

# Setting the parameters
p <- 100
n <- 30
n.test <- 500
sparsity <- 0.2
rho <- 0.5
SNR <- 3

# Generating the coefficient
p.active <- floor(p*sparsity)
a <- 4*log(n)/sqrt(n)
neg.prob <- 0.2
nonzero.betas <- (-1)^(rbinom(p.active, 1, neg.prob))*(a + abs(rnorm(p.active)))

# Correlation structure
Sigma <- matrix(0, p, p)
Sigma[1:p.active, 1:p.active] <- rho
diag(Sigma) <- 1
true.beta <- c(nonzero.betas, rep(0 , p - p.active))

# Computing the noise parameter for target SNR
sigma.epsilon <- as.numeric(sqrt((t(true.beta) %*% Sigma %*% true.beta)/SNR))

# Simulate some data
set.seed(1)
x.train <- mvnfast::rmvn(n, mu=rep(0,p), sigma=Sigma)
y.train <- 1 + x.train %*% true.beta + rnorm(n=n, mean=0, sd=sigma.epsilon)
x.test <- mvnfast::rmvn(n.test, mu=rep(0,p), sigma=Sigma)
y.test <- 1 + x.test %*% true.beta + rnorm(n.test, sd=sigma.epsilon)

# Stepwise Split Regularized Regression
step.out <- cv.stepSplitReg(x.train, y.train, n_models = c(2, 3), max_variables = NULL, keep = 4/4,
                            model_criterion = c("F-test", "RSS")[1],
                            stop_criterion = c("F-test", "pR2", "aR2", "R2", "Fixed")[1], 
                            stop_parameter = 0.05, 
                            shrinkage = TRUE, alpha = 4/4, include_intercept = TRUE, 
                            n_lambda = 50, tolerance = 1e-2, max_iter = 1e5, n_folds = 5, 
                            model_weights = c("Equal", "Proportional", "Stacking")[1], 
                            n_threads = 1)
step.coefficients <- coef(step.out, group_index = 1:step.out$n_models_optimal)
step.predictions <- predict(step.out, x.test, group_index = 1:step.out$n_models_optimal)
mspe.step <- mean((step.predictions-y.test)^2)/sigma.epsilon^2